I created this blog site primarily to share my thoughts, ideas, and work using deep neural networks on speech recognition. I also hope to share tasty tidbits of information regarding the general use of machine learning techniques that I find particularly useful and/or interesting.
A little bit about myself:
I am a doctoral candidate in the Speech, Language, and Hearing sciences department at the University of Connecticut. My research, in broad strokes, deals with how people are able to accommodate ambiguity in spoken and written language. In addition to this general topic, I also investigate how language ability influences talker recognition. I am a cognitive neuroscientist by training and a programmer by nature.
So where does machine learning and neural networks come into all this? It's like this: these techniques are currently utilized by companies like Google and Apple in their voice recognition systems, and they do quite well. My question is why do they work so well? What allows these systems to function as well as they do? I believe that these networks have a lot of information that can be used to help further research on how humans are able to perform very well across a multitude of linguistic (language-y) environments. Specifically, the features used by these networks could prove to be very useful in finding out and testing what invariable cues humans use. In addition, there is plenty of room for the use of these networks to help parameterize languages of the world. What specifically makes languages different from one another? Why is it so hard to learn certain languages, when knowing another?
Through these posts, I hope to foster discussion regarding the use of machine learning, specifically deep learning, in the study of language.
Hope you visit again soon!